Prime Time for Crime?
Arun, Colin, Hongchao, Jack
R Configuration
Below we display our sessionInfo().
sessionInfo(package=NULL)## R version 3.3.2 (2016-10-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] backports_1.0.5 magrittr_1.5 rprojroot_1.2 tools_3.3.2
## [5] htmltools_0.3.5 yaml_2.1.14 Rcpp_0.12.10 stringi_1.1.5
## [9] rmarkdown_1.4 knitr_1.15.1 stringr_1.2.0 digest_0.6.12
## [13] evaluate_0.10
Introduction
Crime has always been a fascinating topic of discussion. It is human nature to pay attention to gruesome murders and moral corruptness. Why? We donât know. However, we do know that through media outlets, society has developed an ideology that unemployment leads to higher crime rates. Is this true? Through our R Notebook, we try to show the various relationships between crime and unemployment, as well as independent crime rate and state level monetary analysis in an effort to better understand the truth behind these âideologies.â We will demonstrate step-by-step instructions on how we created various graphs and charts in Tableau as well as explore the extraordinary visualizations produced through Shiny.
Data
Our employment dataset came from the University of Kentucky Center for Poverty Research (UKCPR). We only focused on the columns that were related through a monetary basis (roughly the first 12 columns). Our crime data came from the U.S. Department of Justice, FBIâs annual Uniform Crime Reporting Statistics. We retrieved the data for all the crime categories listed on the website. Both come from very credible and respected institutions. Thus, the data is very reliable for this project.
ETL
For both datasets we run the relevant ETL operations. We clean the data by first removing special characters (e.g. - ~) from the column names. We then decide which columns are measures and which are dimensions. For dimensions, we change NA to an empty string, change “&” to “and”, change “:” to “;”. We get rid of " and ’. For measures, we change NA to 0. We get rid of all characters except for numbers and the - sign.
## [1] "Population"
## [1] "Employment"
## [1] "Unemployment"
## [1] "Unemployment.rate"
## [1] "Marginally.Food.Insecure"
## [1] "Food.Insecure"
## [1] "Very.Low.Food.Secure"
## [1] "Gross.State.Product"
## [1] "Number.of.low.income.uninsured.children"
## [1] "Percent.Low.Income.Unisured.Children"
## [1] "Personal.income"
## [1] "Workers..compensation"## state_name state year Population
## Alabama : 5 1 : 5 2010:51 Min. : 564516
## Alaska : 5 10 : 5 2011:51 1st Qu.: 1623796
## Arizona : 5 11 : 5 2012:51 Median : 4382667
## Arkansas : 5 12 : 5 2013:51 Mean : 6158836
## California: 5 13 : 5 2014:51 3rd Qu.: 6789176
## Colorado : 5 14 : 5 Max. :38792291
## (Other) :225 (Other):225
## Employment Unemployment Unemployment.rate
## Min. : 283744 Min. : 11152 Min. : 2.700
## 1st Qu.: 730472 1st Qu.: 54283 1st Qu.: 6.000
## Median : 1877812 Median : 157581 Median : 7.300
## Mean : 2796855 Mean : 244360 Mean : 7.368
## 3rd Qu.: 3235551 3rd Qu.: 288906 3rd Qu.: 8.650
## Max. :17348645 Max. :2244326 Max. :13.500
##
## Marginally.Food.Insecure Food.Insecure Very.Low.Food.Secure
## Min. :11.71 Min. : 7.883 Min. :2.036
## 1st Qu.:22.16 1st Qu.:13.051 1st Qu.:4.402
## Median :25.47 Median :15.394 Median :5.378
## Mean :25.45 Mean :15.362 Mean :5.368
## 3rd Qu.:28.50 3rd Qu.:17.266 3rd Qu.:6.159
## Max. :41.08 Max. :25.224 Max. :9.197
##
## Gross.State.Product Number.of.low.income.uninsured.children
## Min. : 26570 Min. : 1.00
## 1st Qu.: 76363 1st Qu.: 14.00
## Median : 190304 Median : 44.00
## Mean : 315502 Mean : 83.01
## 3rd Qu.: 404487 3rd Qu.: 85.00
## Max. :2311616 Max. :843.00
##
## Percent.Low.Income.Unisured.Children Personal.income
## Min. : 0.700 Min. :2.561e+07
## 1st Qu.: 3.000 1st Qu.:6.498e+07
## Median : 4.100 Median :1.664e+08
## Mean : 4.756 Mean :2.685e+08
## 3rd Qu.: 6.100 3rd Qu.:3.427e+08
## Max. :15.000 Max. :1.978e+09
##
## Workers..compensation
## Min. : 7907
## 1st Qu.: 36316
## Median : 110973
## Mean : 298914
## 3rd Qu.: 246610
## Max. :2443512
## ## state_name state year Population Employment Unemployment
## 5 California 5 2010 37334079 16091945 2244326
## 56 California 5 2011 37700034 16258133 2156967
## 107 California 5 2012 38056055 16602672 1921121
## 158 California 5 2013 38414128 16958735 1665590
## 209 California 5 2014 38792291 17348645 1406380
## Unemployment.rate Marginally.Food.Insecure Food.Insecure
## 5 12.2 29.61473 18.56144
## 56 11.7 31.61105 19.07340
## 107 10.4 27.89361 16.48582
## 158 8.9 25.53890 15.72165
## 209 7.5 24.16489 13.69295
## Very.Low.Food.Secure Gross.State.Product
## 5 5.71067 1953411
## 56 6.10045 2030468
## 107 5.79252 2125717
## 158 5.06253 2202678
## 209 4.15612 2311616
## Number.of.low.income.uninsured.children
## 5 763
## 56 770
## 107 653
## 158 488
## 209 341
## Percent.Low.Income.Unisured.Children Personal.income
## 5 7.8 1617134250
## 56 7.8 1727433579
## 107 6.7 1838567162
## 158 5.0 1861956514
## 209 4.0 1977923740
## Workers..compensation
## 5 2067143
## 56 2062255
## 107 2042670
## 158 1990609
## 209 2072792## [1] "Population"
## [1] "Violent.crime.total"
## [1] "Murder.and.nonnegligent.Manslaughter"
## [1] "Legacy.rape..1"
## [1] "Revised.rape..2"
## [1] "Robbery"
## [1] "Aggravated.assault"
## [1] "Property.crime.total"
## [1] "Burglary"
## [1] "Larceny.theft"
## [1] "Motor.vehicle.theft"
## [1] "Violent.Crime.rate"
## [1] "Murder.and.nonnegligent.manslaughter.rate"
## [1] "Legacy.rape.rate..1"
## [1] "Revised.rape.rate..2"
## [1] "Robbery.rate"
## [1] "Aggravated.assault.rate"
## [1] "Property.crime.rate"
## [1] "Burglary.rate"
## [1] "Larceny.theft.rate"
## [1] "Motor.vehicle.theft.rate"## State Population Violent.crime.total
## Alabama : 5 Min. : 564554 Min. : 622
## Alaska : 5 1st Qu.: 1623654 1st Qu.: 5386
## Arizona : 5 Median : 4379730 Median : 15452
## Arkansas : 5 Mean : 6157436 Mean : 23812
## California: 5 3rd Qu.: 6784338 3rd Qu.: 27735
## Colorado : 5 Max. :38802500 Max. :164133
## (Other) :225
## Murder.and.nonnegligent.Manslaughter Legacy.rape..1 Revised.rape..2
## Min. : 7.0 Min. : 99 Min. : 110.0
## 1st Qu.: 51.5 1st Qu.: 533 1st Qu.: 772.8
## Median : 160.0 Median :1190 Median : 1592.0
## Mean : 285.6 Mean :1651 Mean : 2258.2
## 3rd Qu.: 389.0 3rd Qu.:2012 3rd Qu.: 2518.0
## Max. :1884.0 Max. :8398 Max. :11527.0
## NA's :153
## Robbery Aggravated.assault Property.crime.total Burglary
## Min. : 53 Min. : 432 Min. : 9551 Min. : 1689
## 1st Qu.: 1039 1st Qu.: 3376 1st Qu.: 42299 1st Qu.: 8058
## Median : 3689 Median : 9550 Median : 125377 Median : 26196
## Mean : 6862 Mean :14761 Mean : 172925 Mean : 39707
## 3rd Qu.: 7358 3rd Qu.:18087 3rd Qu.: 204282 3rd Qu.: 47991
## Max. :58116 Max. :95877 Max. :1049465 Max. :245767
##
## Larceny.theft Motor.vehicle.theft Violent.Crime.rate
## Min. : 7273 Min. : 244 Min. : 99.3
## 1st Qu.: 29452 1st Qu.: 3792 1st Qu.: 256.4
## Median : 89103 Median : 8626 Median : 329.5
## Mean :119222 Mean : 13996 Mean : 372.7
## 3rd Qu.:143460 3rd Qu.: 15407 3rd Qu.: 449.4
## Max. :654626 Max. :168608 Max. :1326.8
##
## Murder.and.nonnegligent.manslaughter.rate Legacy.rape.rate..1
## Min. : 0.900 Min. : 9.70
## 1st Qu.: 2.500 1st Qu.:23.90
## Median : 4.200 Median :29.00
## Mean : 4.401 Mean :30.74
## 3rd Qu.: 5.600 3rd Qu.:36.05
## Max. :21.800 Max. :89.10
##
## Revised.rape.rate..2 Robbery.rate Aggravated.assault.rate
## Min. : 13.30 Min. : 9.10 Min. : 60.0
## 1st Qu.: 32.08 1st Qu.: 54.60 1st Qu.:153.4
## Median : 38.00 Median : 85.10 Median :218.5
## Mean : 41.34 Mean : 96.13 Mean :236.9
## 3rd Qu.: 47.55 3rd Qu.:117.95 3rd Qu.:296.2
## Max. :125.50 Max. :715.00 Max. :626.1
## NA's :153
## Property.crime.rate Burglary.rate Larceny.theft.rate
## Min. :1524 Min. : 257.2 Min. :1161
## 1st Qu.:2260 1st Qu.: 439.2 1st Qu.:1606
## Median :2726 Median : 568.3 Median :1938
## Mean :2802 Mean : 621.6 Mean :1972
## 3rd Qu.:3305 3rd Qu.: 796.5 3rd Qu.:2289
## Max. :5182 Max. :1157.6 Max. :4082
##
## Motor.vehicle.theft.rate Year
## Min. : 38.9 2010:51
## 1st Qu.:138.4 2011:51
## Median :198.2 2012:51
## Mean :208.1 2013:51
## 3rd Qu.:253.1 2014:51
## Max. :835.7
## ## State Population Violent.crime.total
## 44 Texas 25253466 113231
## 95 Texas 25631778 104734
## 146 Texas 26060796 106475
## 197 Texas 26505637 108757
## 248 Texas 26956958 109414
## Murder.and.nonnegligent.Manslaughter Legacy.rape..1 Revised.rape..2
## 44 1249 7622 NA
## 95 1130 7486 NA
## 146 1148 7715 NA
## 197 1140 7610 10456
## 248 1184 8236 11393
## Robbery Aggravated.assault Property.crime.total Burglary Larceny.theft
## 44 32843 71517 951246 228597 654626
## 95 28620 67498 892810 215755 613131
## 146 30385 67227 876459 205002 606425
## 197 31810 65351 862289 191062 605440
## 248 31181 65656 813934 169234 576154
## Motor.vehicle.theft Violent.Crime.rate
## 44 68023 448.4
## 95 63924 408.6
## 146 65032 408.6
## 197 65787 410.3
## 248 68546 405.9
## Murder.and.nonnegligent.manslaughter.rate Legacy.rape.rate..1
## 44 4.9 30.2
## 95 4.4 29.2
## 146 4.4 29.6
## 197 4.3 28.7
## 248 4.4 30.6
## Revised.rape.rate..2 Robbery.rate Aggravated.assault.rate
## 44 NA 130.1 283.2
## 95 NA 111.7 263.3
## 146 NA 116.6 258.0
## 197 39.4 120.0 246.6
## 248 42.3 115.7 243.6
## Property.crime.rate Burglary.rate Larceny.theft.rate
## 44 3766.8 905.2 2592.2
## 95 3483.2 841.7 2392.1
## 146 3363.1 786.6 2327.0
## 197 3253.2 720.8 2284.2
## 248 3019.4 627.8 2137.3
## Motor.vehicle.theft.rate Year
## 44 269.4 2010
## 95 249.4 2011
## 146 249.5 2012
## 197 248.2 2013
## 248 254.3 2014Tableau Visualizations
Crosstab: Map of Robbery vs Unemployment
Description:
This is a map of Robbery vs Unemployment per year (between 2010 and 2014). The darker the color, the higher rate of robbery per unemployment there is for each state. Notice how as we go from 2010 to 2014, the Robbery vs Unemployment rate grows spreads from surrounding areas of Nevada, DC, and Louisiana, like a virus!
Steps:
- Double Click on State to show the U.S. Map
- Click on Dimension and Create a Calculated FIeld: the formula is: SUM([Robbery])/SUM([Unemployment])
- Drag âYear1â to pages
- Drag the calculated field from step 2 onto Color, customize step/color to personal taste.
- Drag âState Nameâ for label.
- Drag âPopulationâ for detail.
- Drag âStateâ to Filter, and filter out Hawaii and Alaska (We are just going to look at mainland USA)
Histogram: Burglary Rate vs State
Description:
This is a histogram, with dots showing the burglary rate (# of burglaries/100k people) per year (between 2010 and 2014). The line represents the average burglary rate. Notice how as we go from 2010 to 2014, the burglary rates decrease significantly.
Steps:
- Click on Analysis and Unselect âAggregated Measuresâ
- Drag âState Nameâ to Columns
- Drag âBurglary Rateâ to Rows
- Drag âYearâ to Pages
- Drag Measure Names to âColorâ, change color as desired
- Go to Analytics and drag Average Line onto graph and select âPane Lineâ
Scatterplot: Aggravated Assaults and Robbery
Description:
This is a scatterplot showing the relationship between Aggravated Assaults and Robbery per year (between 2010 and 2014). We can see a strong positive linear association between Robberies and Aggravated Assaults through the trend line displayed.
Steps:
- Click on Analysis and unselect âAggregated Measuresâ
- Drag âAggravated Assaultsâ to Rows
- Drag âRobberiesâ to Columns
- Drag âYearâ to Pages
- Right click on the graph and click âTrend Lines,â unselect confidence band. The slope seems to be nearly the same for 2010 to 2013. However, the slope is a little steeper in the year 2014, which means that the aggravated assaults is higher than the previous years.
Boxplot: Year vs Property Crime Rate
Description:
This is a boxplot of property crime rate based on each year. We can see that the property crime rate is slowly decreasing (median wise) as the years progress. DC has the highest property crime rate for all years. They only become outliers for the years: 2012, 2013, and 2014. This means the property crime rate actually decreases for all the other states per year, which makes DC an outlier.
Steps:
- Click on Analysis and unselect âAggregated Measuresâ
- Drag âYearâ to columns
- Drag âProperty Crime Rateâ to rows
- Drag âYearâ to color
- Drag âStateâ to Detail
- Go to âAnalyticsâ and drag average line to the chart and select Pane Line
- Click on Box Plot on the righthand side.
Barchart: State and Year vs Burglaries
Description:
This is a bar chart showing the number of burglaries on each state per year. We manually filtered by big east coast states and west coast states. Notice how West Coast burglaries is significantly higher (around 30k more) than the East Coast burglaries. This could be because of the small number of states classified as âwest coast,â giving a large standard deviation.
Steps:
- Click on Analysis and unselect âAggregated Measuresâ
- Drag âYearâ and âStateâ to Rows
- Drag âBurglariesâ to Columns
- Right click on âBurglariesâ and select âMeasuresâ then âAverageâ
- Drag âStateâ to Filter and select designated states.
- Go to âAnalyticsâ and drag average line to the chart and select Pane Line
Shiny Application
Crosstab: Crime to employment ratio
Description:
The Crosstab plots a Crime to Employment Ratio and analyzes every state over the course of 2010-2014. Results indicate that D.C has the highest crime rate by far. Despite changing the sliders multiple times, the area stays with a high KPI. Most nations experience a medium KPI under most settings. The most peaceful nations seem to be Vermont and Wyoming. Vermont especially keeps a very low KPI in almost all settings.
Histogram: Employees aggregated every year
Description:
The histogram outlines number of employees aggregate every year from 2010 to 2014. Each year has a different histogram. The bucket size for the histograms are 200000 because of how varied populations are across the states. The biggest increase in overall employment happened between 2010 to 2011, though this growth was slow. After that employment seems to have been increasing very slowly the next 2 years.
Scatterplot: Food Insecurity vs Violent Crime Rate
Description:
The scatter plots a food insecurity ratio on the Y axis and the violent crime rate on the X axis. The only real outlier in terms of results is California which experiences a lower food insecurity but the highest crime rate by far. In terms of interesting results, Mississippi experiences a huge drop in food insecurity after 2010 and then slowly goes back up starting in 2011. Itâs interesting because it starts off as the highest in food insecurity and then goes down by a lot, but the violent crime rate stays the same. 2014 seems to indicate that food insecurity on average has gone up by a lot more than in past years. There are very few states with a 10 or less food insecurity ratio.
Boxplot: State vs GSP
Description:
The boxplot outlines each independent state and their GSP (Gross State Products) over 2010-2014.We wanted to let you hover over each point to show the year, but couldn’t find a way to do it directly through geom_boxplot.Our workaround was using geom_point, so if you hover over those points you can see the year of each value. Zoom in for the smaller states for more information. California has by far the largest GSP with its lowest value of 1953411 being higher than any other state’s GSP . Texas comes in second place followed by New York. These states also have the biggest gaps between the top and bottom of their boxes. The smaller GSPâs are all much more knit together. Vermont has all 5 of the lowest GSP values, but itâs GSP has been going up over the past few years.
Barchart: Violent Crimes Per Year
Description:
The black on the barchart represents the total number of violent crimes done. Each state has an independent graph showcasing the number of crimes done from the years 2010-2014. The red line represents the mean number of violent crimes done in that particular state over the timespan. The blue line represents the difference between the mean and the respective year. The range between the state with the least number of crimes and the highest number of crimes was shocking. California has averaged close to 158000 crimes a year while Vermont has averaged only around 800 a year. Outside of that, the results seemed to indicate that murder rate has stayed fairly consistent in most of the US around the time period.
Conclusion
Through the data, one can see many interesting observations. From Robberies being spread from surrounding areas of Nevada, DC, and Louisiana to other states to how west coast burglaries are significantly more than east coast burglaries, we are able to get a broad idea of the rate at which crimes are happening in recent years. Regardless of how a state is doing, in terms of monetary stability or unemployment rates, we have observed burglary rates and property crime rates decreasing throughout the years. It is safe to assume, that as the years increase, given all situations are static, that the following years to come wonât be a âPrime Time for Crime.â
Link to Shiny Application
Here is the website to the shiny application : https://tesseract2010.shinyapps.io/finalproject/